One advantage of a rule based approach is that rules can be derived using unlabelled text only, which are much easier to obtain. For example, [1]} build a named entity classifier using \(90,000\)  unlabelled examples. They start from 7 seed rules (`New York', `California' and `U.S.' are locations; any name containing `Mr.' is a person; any name containing `Incorporated' is an organisation; `I.B.M.' and `Microsoft' are organisations), which is the only supervision in their approach. The classifier, automatically inducing new spelling rules and contextual rules, finally achieves over \(91\%\)  accuracy when evaluated on a test set of \(1,000\)  manually labelled instances.
